A Spectral Model-Informed Neural Network for Inverse Source Problems

Abstract

In this paper, we propose an unsupervised, two-step model-informed deep learning framework for solving the inverse source problem. In the first step, boundary measurement data are transformed into an imaging function that encodes information about the shape and location of the unknown source. Leveraging this information, we derive a Fourier-based model equation in the spectral domain that relates the imaging function to the Fourier coefficients of the source function. In the second step, this model equation is incorporated into the training of a model-informed neural network to recover the parameters of interest. The proposed approach enables fast and accurate reconstruction of the source function while maintaining robustness to noise. The effectiveness of the method is demonstrated through numerical experiments in both two- and three-dimensional settings. For the two-dimensional case, we further compare our approach with the traditional least-squares method to validate its computational efficiency and reconstruction accuracy.

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BibTeX RIS

BibTeX

@misc{nguyen2026spectral,
  title = {{A Spectral Model-Informed Neural Network for Inverse Source Problems}},
  author = {Dinh-Liem Nguyen and Nhung H. Nguyen and Aravinth Ravi},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.33062},
  eprint = {2609.33062},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.33062},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - A Spectral Model-Informed Neural Network for Inverse Source Problems
AU  - Dinh-Liem Nguyen
AU  - Nhung H. Nguyen
AU  - Aravinth Ravi
PY  - 2026
DA  - 2026/09/27
PB  - arXiv
DO  - 10.48550/arxiv.2609.33062
UR  - https://arxiv.org/abs/2609.33062
AB  - In this paper, we propose an unsupervised, two-step model-informed deep learning framework for solving the inverse source problem. In the first step, boundary measurement data are transformed into an imaging function that encodes information about the shape and location of the unknown source. Leveraging this information, we derive a Fourier-based model equation in the spectral domain that relates the imaging function to the Fourier coefficients of the source function. In the second step, this model equation is incorporated into the training of a model-informed neural network to recover the parameters of interest. The proposed approach enables fast and accurate reconstruction of the source function while maintaining robustness to noise. The effectiveness of the method is demonstrated through numerical experiments in both two- and three-dimensional settings. For the two-dimensional case, we further compare our approach with the traditional least-squares method to validate its computational efficiency and reconstruction accuracy.
ER  -

Abstract

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